Chalcopyrite Bioleaching Using Adapted Mesophilic Microorganisms: Effects of Temperature, Pulp Density, and Initial Ferrous Concentrations
Bibliographic record
Abstract
This study examines the effects of several operating parameters on copper leaching from chalcopyrite ores using an adapted mesophilic bacterial culture. Three temperatures (35, 40, and 45°C), three pulp density (1, 2, and 4% (w/v)), and three initial ferrous ion (Fe(II)) concentrations (5, 10, and 20 g/L) were employed as variable parameters, and their effects on the bioleaching efficiency of chalcopyrite were investigated. After 14 days, the maximum copper bioleaching efficiency was estimated to be ∼64% at a temperature of 45°C, a pH of 1.5, an initial ferrous concentration of 5 g/L, and a pulp density of 4%. More specifically, the chalcopyrite dissolution tests conducted at different temperatures showed a minimal effect of temperature and low leaching efficiency (<20%) regardless of temperature. The trend of chalcopyrite dissolution at different pulp densities showed that Cu extraction tended to increase with increases in pulp density. Moreover, the Cu leaching efficiency associated with mesophilic microorganisms largely decreased when the initial Fe(II) concentration was greater than 10 g/L. The Cu leaching behavior in different test conditions was evalauted with concentrations of total iron (Fe), Fe(II), and ferric ions (Fe(III)), as well as the oxidation-reduction potential (ORP) of the solution used in the test. The Cu leaching rate increased under lower ORP conditions, lower Fe(III):Fe(II) ratios, and balanced Fe(II)–Fe(III) cycles.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".